We begin by introducing the generative grammar and how to invert symbols and rules to obtain neural-symbolic capsules (Section 3). They are internally different to the ones proposed by Hinton et al. [1]}, as they essentially act as containers for regression models. Next, we present a modified routing-by-agreement and training protocol (Section 4), coupled to a lifelong meta-learning pipeline (Section 5). Through meta-learning, the capsule network continuously grows and trains individual capsules. We finally demonstrate the potential of the approach by presenting some results based on an "Asteroids"-like environment (Section 6) before ending with a conclusion.
